Sentence examples for equivocal category from inspiring English sources

Nineteen of 25 results in the 2007 equivocal category were reassigned as positive (n = 13) or negative (n = 6).

In our analysis, we found that RENEX has better agreement with consensus and experts in obstructed and non-obstructed categories, but not in the equivocal category.

In particular, RENEX and expert consensus rated the renal scans with high agreement in the non-obstructed and obstructed categories while they did not seem to agree well in the equivocal category.

We have previously shown that there is a need for an equivocal category in the reading of bone scans to avoid diagnostic misclassification [17]; we encourage the inclusion of this category for the classification of SPECT/CT images of the bone, as well.

There was also a variation in the extension of HER2 immunoreactive areas in the equivocal category, ranging from 10to40%0 % of tumor cells.

In our study, approximately 8% of breast cancer patients had weak positive or borderline staining (2+) for HER2, which was interpreted as an equivocal category that would be recommended for verification with fluorescent in situ hybridization (FISH) for therapeutic indication of trastuzumab (Herceptin) treatment [ 15].

Tests of hypothesis suggest that agreement among three experts is the same as agreement among two experts and RENEX for the non-obstructed (p = 0.79), obstructed (p = 0.49) and equivocal categories (p = 0.03).

The regression coefficients for the non-obstructed, equivocal, and obstructed categories were, respectively, 1.57 (p < 0.0001), -0.28 (p = 0.37), and 1.82 (p < 0.0001).

Moreover, the pairwise agreement between RENEX and an expert are also the same (p = 0.47, 0.34, 0.58, respectively, for the non-obstructed, equivocal and obstructed categories).

Tests of hypothesis in Table 3 indicated that for all response categories, the pairwise agreement among experts was the same (p = 0.15, 0.08, 0.11, respectively, for the non-obstructed, equivocal, and obstructed categories).

From 641 US reports (181/641 patients with AA, that is 28.2 %), data were used to generate a logistic predictive model to define negative, equivocal and positive categories for the diagnosis of AA [22].